Graduation Year

2004

Document Type

Thesis

Degree

M.S.E.M.

Degree Granting Department

Engineering Management

Major Professor

Ali Yalcin.

Keywords

disaster management, decision making, disaster relief, humanitarian assistance, decision under uncertainty

Abstract

With the increase in the occurrence of disasters (natural and man-made) that leave people injured, handicapped or dead, the disaster management theory is gaining more importance. As a consequence, human assistance and disaster relief organizations are managing increasingly more inventories anticipated to help people in need. Donations are the common means used by humanitarian relief organizations for procuring commodities to support some of their programs. Previous experiences have indicated that donations become a burden instead of offering relief when they do not match actual victims' needs. Accepting or rejecting donations is a key issue that can produce not only economic losses but loss of lives as well. The objective of this thesis is to provide a means of assessing acceptance or rejection decisions using decision tree analysis theory and utility theory. The proposed model considers the inputs that a decision-maker may face when accepting or rejecting a donation.

Such inputs include these categories: the probability of the occurrence of disaster, the need for and further use of a commodity, the unit price and holding cost of the item, the benefit provided by the donation, and the probability of having subsequent donations when the initial donation is initially rejected. Various scenarios are simulated in Excel® environment through the Monte Carlo process. This will assess the varied impacts from the alternative inputs in the decision making process; a sensitivity analysis will evaluate the effects of various decisions. The results obtained from the simulation of the diverse scenarios indicate that the decision of accepting or rejecting donations is driven more by the possibility of the use of the commodity than by the probability of occurrence of the disaster.

The findings from the model also indicate that the decision of accepting or rejecting is more sensitive to the relationship of sale price to benefit deployment of the commodity than to sale price alone. The simulation of the expected monetary benefit of the relief provided results in the development of graphs that can affect the decision making process when accepting or rejecting donations.

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